Joel Chao
Senior Staff ML Scientist at Appier
New Taipei, Taiwan
Summary
👤
Senior🎓
Top SchoolJoel Chao is a machine learning leader with over a decade of experience spanning research and production AI, currently guiding a 10+ person ML team at Dcard in New Taipei, Taiwan. He has delivered end-to-end ML initiatives—from data pipelines and experimentation design to product roadmaps and large-scale recommendation systems, including real-time purchase-intent prediction and CTR-focused optimization. His background spans NLP and multilingual recommendation work at Baidu, as well as scalable ML projects at Appier, showcasing versatility across speech/text, web search, and graph-based representations. An active open-source contributor, he has improved the Keras library as a ML Engineer and Test Automation Engineer, fixing Inception V3 issues and strengthening the test suite. He holds a Master's degree in Computer Science from National Taiwan University, merging strong academic training with hands-on software engineering to deliver production-grade AI solutions.
11 years of coding experience
10 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at National Taiwan University
Chinese, English
Github Skills (13)
keras10
tensorflow210
machine-learning10
deep-learning10
tensorflow10
neural-networks10
pytest10
python10
testing10
convolutional-neural-network9
neuralnetwork9
neural-network9
convolutional-neural-networks9
Programming languages (3)
Github contributions (5)
keras-team/keras
Apr 2016 - Aug 2018
Deep Learning for humans
Role in this project:
ML Engineer & Test Automation Engineer
ML Engineer & Test Automation EngineerContributions:13 commits, 22 PRs, 354 comments in 2 years 4 months
Contributions summary:Joel primarily contributed to improving the Keras library by fixing bugs and implementing new features. Their work involved correcting the Inception V3 network and addressing zero division errors in the merge mode for 'cos'. They also worked on the test suite, particularly focusing on fixing image path errors and refactoring local tests. Their contributions span model architecture adjustments and ensuring code quality through testing.
pythondata-sciencedeep-learningneural-networksmachine-learning
joelthchao/tensorflow-finetune-flickr-style
Feb 2016 - Aug 2017
Contributions:12 commits, 2 PRs, 11 pushes in 1 year 6 months
finetunetensorflow
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